Spectral Clustering Algorithm for Cognitive Diagnostic Assessment

Frontiers in Psychology

Jing Yang³  Lei Guo⁴  Naiqing Song⁵ 

[1] Basic Education Research Center, Southwest University, Chongqing, China;Faculty of Psychology, Southwest University, Chongqing, China;School of Mathematics and Statistics, Northeast Normal University, Changchun, China;Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Chongqing, China;Urban and Rural Education Research Center, Southwest University, Chongqing, China;

关键词: cognitive diagnostic assessment;    spectral clustering;    K-means;    G-DINA model;    classification accuracy;   

DOI  :  10.3389/fpsyg.2020.00944

来源: DOAJ

【 摘 要 】

In cognitive diagnostic assessment (CDA), clustering analysis is an efficient approach to classify examinees into attribute-homogeneous groups. Many researchers have proposed different methods, such as the nonparametric method with Hamming distance, K-means method, and hierarchical agglomerative cluster analysis, to achieve the classification goal. In this paper, according to their responses, we introduce a spectral clustering algorithm (SCA) to cluster examinees. Simulation studies are used to compare the classification accuracy of the SCA, K-means algorithm, G-DINA model and its related reduced cognitive diagnostic models. A real data analysis is also conducted to evaluate the feasibility of the SCA. Some research directions are discussed in the final section.

【 授权许可】

Unknown